Summary of A Survey on Compositional Learning Of Ai Models: Theoretical and Experimental Practices, by Sania Sinha et al.
A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices
by Sania Sinha, Tanawan Premsri, Parisa Kordjamshidi
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper surveys the literature on compositional learning in AI models, exploring connections between cognitive and linguistic studies. It identifies abstract concepts of compositionality and connects them to computational challenges faced by language and vision models. The authors overview formal definitions, tasks, evaluation benchmarks, various models, and theoretical findings. They focus on linguistic benchmarks and combining language and vision, highlighting compositional capabilities exhibited by state-of-the-art AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can learn to combine simple ideas into more complex ones. This is important because it’s a key part of human intelligence, especially for understanding language and pictures. The researchers review what’s been done so far in this area and identify some areas where machine learning could be improved. They also explore the connections between machine learning and how humans think. |
Keywords
» Artificial intelligence » Machine learning
